SOTAVerified

Meta-Learning

Meta-learning is a methodology considered with "learning to learn" machine learning algorithms.

( Image credit: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks )

Papers

Showing 601625 of 3569 papers

TitleStatusHype
Context-Aware Meta-LearningCode1
Laplacian Regularized Few-Shot LearningCode1
Learn From Model Beyond Fine-Tuning: A SurveyCode1
DPGN: Distribution Propagation Graph Network for Few-shot LearningCode1
ARCADe: A Rapid Continual Anomaly DetectorCode1
Learning Compositional Rules via Neural Program SynthesisCode1
Architecture, Dataset and Model-Scale Agnostic Data-free Meta-LearningCode1
Data-Efficient Brain Connectome Analysis via Multi-Task Meta-LearningCode1
Adapting to Distribution Shift by Visual Domain Prompt GenerationCode1
Learning Normal Dynamics in Videos with Meta Prototype NetworkCode1
Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement LearningCode1
Learning to Adapt to Evolving DomainsCode1
L2B: Learning to Bootstrap Robust Models for Combating Label NoiseCode1
Learning to Compare: Relation Network for Few-Shot LearningCode1
On the Convergence Theory for Hessian-Free Bilevel AlgorithmsCode1
Continued Pretraining for Better Zero- and Few-Shot PromptabilityCode1
Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and AdvertisingCode1
Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link PredictionCode1
Data Augmentation for Meta-LearningCode1
Continuous Optical Zooming: A Benchmark for Arbitrary-Scale Image Super-Resolution in Real WorldCode1
AReLU: Attention-based Rectified Linear UnitCode1
Learning to Generalize Unseen Domains via Memory-based Multi-Source Meta-Learning for Person Re-IdentificationCode1
Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-LearningCode1
Learning to Learn to Disambiguate: Meta-Learning for Few-Shot Word Sense DisambiguationCode1
Few-Shot Learning with Class ImbalanceCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8Unverified
2MZMeta-train success rate97.6Unverified
3MAMLMeta-test success rate36Unverified
4RL^2Meta-test success rate10Unverified
5DnCMeta-test success rate5.4Unverified
6PEARLMeta-test success rate0Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60Unverified
2Multi-task multi-head SACAverage Success Rate35.85Unverified
3DisCorAverage Success Rate26Unverified
4NDPAverage Success Rate11Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5Unverified
2MZMeta-test success rate (zero-shot)17.7Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75Unverified